Conventional social matching methods and systems are computer-based platforms that recommend individuals to each other. Such social matching platforms can introduce people both online and in physical spaces, for reasons including, but not limited to, friendship, dating, or professional networking Current social matching solutions cater to subsets of the population that seek connection with others within their population-subset having specific attributes or who are seeking a specific benefit or desired outcome from identifying a matched individual. Such social matching solutions have pre-defined attribute categories allowing users to report their own or their preferred attributes. While such platforms cater to well-defined subsets of individuals, they do not enable individuals to be matched with one another across different subsets of the general population based on a plurality of attributes. Further, having a limited subset of individuals and a limited scope of matching attributes with which to match individuals, the outcomes from social matching are limited, on average, to a well-defined and narrow set of benefits or outcomes for the users. Therefore, current social matching solutions are limited in that they do not address enhancing a range of benefits to individuals resulting from matches. In addition, these platforms can quickly become stale if incentives are not present for individuals to maintain and regularly update their profiles in order to identify new individuals with whom they have not yet been matched. Therefore, there is a need for social matching systems and methods that address matching individuals across the larger general population and that enable matching based on a multitude of attributes and social matching outcomes.
Social matching platforms rely on many factors in order to determine what constitutes a good match. These can include, but are not limited to, attributes such as demographics, user-selected interests, educational background, physical features, and behaviors. These matching factors may be entered by the user, observed by the system, or acquired from external sources such as third parties. For example, an existing social media profile may provide matching factors or attributes. Such approaches to collecting attributes for the purpose of social matching have been limited to an available set of attribute categories associated with a given social matching platform. In these instances, the attribute categories do not address the needs of individuals who seek social matches that lie outside the scope of a platform's given set of attribute categories. Therefore matching platforms and matching factors are restricted to finding other individuals in the social matching service rather than finding third parties, groups or organizations of interest. For example, current platforms are limited in determining if a location, a group, a website, an item, an experience or an event is presently or has been historically frequented by others with whom an individual may be matched. Furthermore, current social matching platforms calculate an affinity or matching score then entities with a score that exceeds some threshold parameter are presented as viable matches to the end user. The details of the match analysis are not disclosed to either entity of the potential match, which makes it difficult to evaluate the match performance or to tailor results for specific objectives. Therefore, current platforms are limited in their ability to enable matching based on a wide-ranging set of parameters and motives and do not provide a detailed match analysis output containing all of the matching attributes and the relative values of each attribute.
Social matching methods and systems typically provide users with access to a mechanism for facilitating introductions with other people. This mechanism is generally available through online websites or through software applications for mobile devices such as smartphones or tablet computers. The mechanism may be as simple as a listing of potential matches or may include search capabilities to further refine the result set. These platforms are limited, however, in that they may not enable multiple modes of one-to-one connectivity between matched individuals. Furthermore, such connectivity does not address having multiple formats of connectivity, privacy settings for communications and discretion with regard to identity revelation between matched individuals. While some individuals are unconcerned with regard to privacy and identity, others prefer anonymity and privacy in communicating with matched individuals. In addition, many social matching platforms reveal the identities of users and provide access to full user profiles of potential matches or identified matches. There are few provisions made to maintain privacy or anonymity among matched individuals. Therefore there is a need for social matching methods and systems that address privacy concerns with regard to communications, identities, and user profile data access.
The triggering of a match analysis can be initiated by the system or by one or more individuals. Current social matching platforms typically alert individuals when they have been matched by the system such that both individuals become aware of each other as a match. There are a number of drawbacks to alerting both parties that a match has been made. For example, if one individual does not find the match desirable, they will be less likely to find the system useful and thus will be less likely to use the platform, or, in cases of matched lists, they may wish to remove the match from their list but may hesitate in doing so if the other individual can become aware of the action taken. Therefore systems and methods that incorporate discretion in qualifying potential matches are desired. These systems and methods would provide for social matching wherein the identified matches are not notified when they have been matched with a match-requesting individual. This enables the match-requesting individual to privately determine if the match is desirable and keep the match or, if the match is undesirable, to remove the match. Therefore, a platform that addresses this drawback may enable different levels of matched-awareness. For example, one party (e.g., the match-requesting party) or if desired, in some instances, two parties (e.g., the match-requesting party and the matched party) may be notified of the match.
Social matching systems utilize a process or algorithm to analyze the matching factors of one individual against all other individuals in order to determine suitability for a match. This process is generally executed on a periodic basis not correlated to when users access the system via the introduction mechanism. These systems are limited, however, since static or batch service matching requests by a social matching system do not enable dynamic user-initiated match requests. Some matching systems rely on physical proximity to alert pre-matched users for a potential introduction, however, this requires both users to have systems actively in use that share or broadcast their location. In addition, an area of concern in social matching systems is the timing upon when match analyses are performed. The social, contextual, historical, and other attributes that together form a user profile may be updated at any time and may be updated many times. A user's history of attributes and this, user profile can be assumed to be changing from moment to moment. Therefore, systems and methods that provide an on-demand or real-time user-initiated requesting capability while using the most recent available user profile attributes and contextual data and without the need for more than one user to be actively engaged on the platform are desired.
There is therefore a need for systems and methods that overcome some or all of the previously delineated drawbacks and limitations of prior social matching platforms, which provides for a social matching platform addressing wide-ranging social needs of the general population with flexible, customizable settings that incorporate the preferences or desired outcomes of different users including individuals or groups.